Due to the high-dimensionality of motion captured data which resulted in the complexity in motion analysis, a method of motion data processing based on manifold learning was proposed. Isomap, a classical manifold learning algorithm, was necessary to be improved and extended in this paper. A framework of motion data processing based on manifold learning was built to embed high-dimensionality data into low-dimensionality space. It simplified the motion analysis, and in the same time preserved the original motion features. In order to solve the inefficiency of processing large-scale motion data, Sample Isomap (S-Isomap) algorithm was proposed. Experiments proved that approximate embeddings of motion data computed by S-Isomap were average 10 times faster than by Isomap, while 10% frame samples were selected. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Li, F., Huang, T., & Li, L. (2007). Method of motion data processing based on manifold learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4469 LNCS, pp. 248–259). Springer Verlag. https://doi.org/10.1007/978-3-540-73011-8_26
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